Review of Research Status of Autonomous Mobile Robot Environment Recognition and Path Planning Algorithms
Abstract
The convergence of Industry 4.0 and the challenges posed by the COVID-19 pandemic amplify the growth prospects and market reach of autonomous mobile robots. This paper elucidates the current research landscape and forward-looking trajectory concerning perception systems for autonomous mobile robots. It advocates a future-oriented development path grounded in the utilization of vision sensors and multi-sensor configurations to enhance environmental recognition. Addressing the imperative of bolstering environmental discernment in intricate settings remains a priority. An emerging area of interest pertains to terrain prediction algorithms. The ensuing section deliberates upon the merits and demerits intrinsic to distinct robot path planning algorithms. These algorithms can be categorized into global path planning algorithms, entrusted with charting optimal overarching courses, and local path planning algorithms, designed to navigate impediments and effect localized route refinements. Envisioning the future, the maturation of robot path planning algorithms is poised to embrace multi-algorithm collaborative applications.
References
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Lebedev, D. V., Steil, J. J., & Ritter, H. J. (2005). The dynamic wave expansion neural network model for robot motion planning in time-varying environments. Neural Networks, 18(3), 267-285. https://doi.org/10.1016/j.neunet.2005.01.004
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Moysis, L., Petavratzis, E., Volos, C., Nistazakis, H., & Stouboulos, L. (2020). A chaotic path planning generator based on logistic map and modulo tactics. Robotics and Autonomous Systems, 124, Article 103377. https://doi.org/10.1016/j.robot.2019.103377
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Phung, M. D., & Ha, Q. P. (2020). Motion-encoded particle swarm optimization for moving target search using UAVs. Applied Soft Computing, 97, Article 106705. https://doi.org/10.1016/j.asoc.2020.106705
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Ren, Z. B., Lam, E. Y., & Zhao, J. L. (2020). Real-Time Target Detection in Visual Sensing Environments Using Deep Transfer Learning and Improved Anchor Box Generation. Ieee Access, 8, 193512-193522. https://doi.org/10.1109/access.2020.3032955
Reza, M., Dehbalaei, G., & Delshad, E. (2013). Presenting a Model for the Affordable Choice of Wiring Route in the Electrical and Telecommunications Networks in the Residential Areas Based on the Artificial Intelligence A-STAR Algorithm. Life Science Journal-Acta Zhengzhou University Overseas Edition, 10(1), 1068-1070.://WOS:000322998200164
Saeed, R. A., Omri, M., Abdel-Khalek, S., Ali, E. S., & Alotaibi, M. F. (2022). Optimal path planning for drones based on swarm intelligence algorithm. Neural Computing & Applications, 34(12), 10133-10155. https://doi.org/10.1007/s00521-022-06998-9
Sangeetha, V., Krishankumar, R., Ravichandran, K. S., & Kar, S. (2021). Energy-efficient green ant colony optimization for path planning in dynamic 3D environments. Soft Computing, 25(6), 4749-4769. https://doi.org/10.1007/s00500-020-05483-6
Seyitoglu, F., & Ivanov, S. (2021). Service robots as a tool for physical distancing in tourism. Current Issues in Tourism, 24(12), 1631-1634. https://doi.org/10.1080/13683500.2020.1774518
Shang, Y., Liu, B. S., Zhang, W. C., & Xu, Y. R. (1998). Autonomous Underwater Vehicles Global Path Planning Using Case-Based Learning Algorithm. JOURNAL OF HARBIN ENGINEERING UNIVERSITY(05), 4-10. https://doi.org/CNKI:SUN:HEBG.0.1998-05-000.
Sharma, V., Kbashi, H. J., & Sergeyev, S. (2021). MIMO-employed coherent photonic-radar (MIMO-Co-PHRAD) for detection and ranging. Wireless Networks, 27(4), 2549-2558. https://doi.org/10.1007/s11276-021-02605-2
Shibata, T., Fukuda, T., & Ieee. (1993, May 02-06). COORDINATIVE BEHAVIOR BY GENETIC ALGORITHM AND FUZZY IN EVOLUTIONARY MULTIAGENT SYSTEM. [Proceedings : Ieee international conference on robotics and automation, vols 1-3]. 1993 Ieee International Conf on Robotics and Automation, Atlanta, Ga.
Song, B. Y., Wang, Z. D., & Zou, L. (2021). An improved PSO algorithm for smooth path planning of mobile robots using continuous high-degree Bezier curve. Applied Soft Computing, 100, Article 106960. https://doi.org/10.1016/j.asoc.2020.106960
Stentz, A., & Ieee. (1994, May 08-13). OPTIMAL AND EFFICIENT PATH PLANNING FOR PARTIALLY-KNOWN ENVIRONMENTS.Ieee International Conference on Robotics and Automation [1994 ieee international conference on robotics and automation: Proceedings, vols 1-4]. 1994 IEEE International Conference on Robotics and Automation, San Diego, Ca.
Sun, B., Chen, W. D., & Xi, Y. G. (2005). Particle Swarm Optimization Based Global Path Planning for Mobile Robots. Control and Decision(09), 1052-1055+1060. https://doi.org/10.13195/j.cd.2005.09.94.sunb.019.
Sun, W. C., Wang, S. Y., Wu, J. C., & Du, X. (2017, Mar 25-26). An improved RGB-D SLAM algorithm. [2017 ieee 2nd advanced information technology, electronic and automation control conference (iaeac)]. 2nd IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, PEOPLES R CHINA.
Tao, Y., Gao, H., Ren, F., Chen, C. Y., Wang, T. M., Xiong, H. G., & Jiang, S. (2021). A Mobile Service Robot Global Path Planning Method Based on Ant Colony Optimization and Fuzzy Control. Applied Sciences-Basel, 11(8), Article 3605. https://doi.org/10.3390/app11083605
Zhang, P. Y., Lu, T. S., & Song, L. B. (2006). The Case-Based Learning of Motion Planning and Its SVR Implementation for Volleyball Robot. Journal of Shanghai Jiaotong University (03), 461-465. https://doi.org/10.16183/j.cnki.jsjtu.2006.03.022.
Zhang, S. C., Pu, J. X., & Si, Y. N. (2021). An Adaptive Improved Ant Colony System Based on Population Information Entropy for Path Planning of Mobile Robot. Ieee Access, 9, 24933-24945. https://doi.org/10.1109/access.2021.3056651
Zhou, H. B., Zhou, S., Yu, J., Zhang, Z. D., & Liu, Z. Z. (2020). Trajectory Optimization of Pickup Manipulator in Obstacle Environment Based on Improved Artificial Potential Field Method. Applied Sciences-Basel, 10(3), Article 935. https://doi.org/10.3390/app10030935
Alcacer, V., & Cruz-Machado, V. (2019). Scanning the Industry 4.0: A Literature Review on Technologies for Manufacturing Systems. Engineering Science and Technology-an International Journal-Jestech, 22(3), 899-919. https://doi.org/10.1016/j.jestch.2019.01.006
Bai, Y., Hou, Y. B., & Ieee. (2017, Jul 10-13). Research of Environmental Modeling Method of Coal Mine Rescue Snake Robot based on Information Fusion. [2017 20th international conference on information fusion (fusion)]. 20th International Conference on Information Fusion (Fusion), Xian, PEOPLES R CHINA.
Bigliardi, B., Bottani, E., & Casella, G. (2019, Nov 20-22). Enabling technologies, application areas and impact of industry 4.0: a bibliographic analysis.Procedia Manufacturing [International conference on industry 4.0 and smart manufacturing (ism 2019)]. International Conference on Industry 4.0 and Smart Manufacturing (ISM), Rende, ITALY.
Bongomin, O., Yemane, A., Kembabazi, B., Malanda, C., Mwape, M. C., Mpofu, N. S., & Tigalana, D. (2020). Industry 4.0 Disruption and Its Neologisms in Major Industrial Sectors: A State of the Art. Journal of Engineering, 2020, Article 8090521. https://doi.org/10.1155/2020/8090521
Chai, H., J., M., Rong, X. W., & Li, Y. B. (2014). Design and Implementation of SCalf, an Advanced Hydraulic Quadruped Robot. Robot, 36(04), 385-391. https://doi.org/10.13973/j.cnki.robot.2014.0385
Chen, C. X., Pei, L., Xu, C. Q., Zou, D. P., Qi, Y. H., Zhu, Y. F., & Li, T. (2019, May 22-25). Trajectory Optimization of LiDAR SLAM Based on Local Pose Graph.Lecture Notes in Electrical Engineering [China satellite navigation conference (csnc) 2019 proceedings, vol i]. 10th China Satellite Navigation Conference (CSNC), Beijing, PEOPLES R CHINA.
Cheng, Y., Bai, J. Q., Xiu, C. B., & Ieee. (2017, May 28-30). Improved RGB-D vision SLAM algorithm for mobile robot.Chinese Control and Decision Conference [2017 29th chinese control and decision conference (ccdc)]. 29th Chinese Control And Decision Conference (CCDC), Chongqing, PEOPLES R CHINA.
deng, X. Q. (2014). Path planning of mobile robot based on modified artificial potential field method. Journal of Shandong University of Technology(Natural Science Edition), 28(01), 38-41. https://doi.org/10.13367/j.cnki.sdgc.2014.01.010
Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 26(1), 29-41. https://doi.org/10.1109/3477.484436
Fratini, L., Ragai, I., & Wang, L. (2020). New trends in Manufacturing Systems Research 2020. Journal of Manufacturing Systems, 56, 585-586. https://doi.org/10.1016/j.jmsy.2020.04.010
Ge, S. S., & Cui, Y. J. (2000). New potential functions for mobile robot path planning. Ieee Transactions on Robotics and Automation, 16(5), 615-620. https://doi.org/10.1109/70.880813
Gong, H. J. (2019). Vision Object Detection and Path Planning Algorithm for Unmanned Vehicle [Master, Beijing Jiaotong University].
Guo, J. C., Gao, Y., & Cui, G. Z. (2013). Path Planning of Mobile Robot Based on Improved Potential Field. Information Technology Journal, 12(11), 2188-2194. https://doi.org/10.3923/itj.2013.2188.2194
Halloush, R. (2016). Overhearing-aware modified Dijkstra's algorithm for multicasting over multi-hop wireless networks. International Journal of Communication Networks and Distributed Systems, 16(3), 240-260. https://doi.org/10.1504/ijcnds.2016.076651
Han, L., & Liu, G. D. (2011). A Local Path Planning Method for Mobile Robots Based on the Windows. Computer Systems & Applications, 20(08), 160-163. https://doi.org/CNKI:SUN:XTYY.0.2011-08-034
Jing, X., Michalewicz, Z., Lixin, Z., & Trojanowski, K. (1997). Adaptive evolutionary planner/navigator for mobile robots. IEEE Transactions on Evolutionary Computation, 1(1), 18-28. https://doi.org/10.1109/4235.585889
Kahlmann, T., Oggier, T., Lustenberger, F., Blanc, N., & Ingensand, H. (2004, Nov 29-Dec 01). 3D-TOF sensors in the automobile.Proceedings of the Society of Photo-Optical Instrumentation Engineers (Spie) [Photonics in the Automobile]. Conference on Photonics in the Automobile, Geneva, SWITZERLAND.
Khatib, O. (1985, 25-28 March 1985). Real-time obstacle avoidance for manipulators and mobile robots. Proceedings. 1985 IEEE International Conference on Robotics and Automation,
Lebedev, D. V., Steil, J. J., & Ritter, H. J. (2005). The dynamic wave expansion neural network model for robot motion planning in time-varying environments. Neural Networks, 18(3), 267-285. https://doi.org/10.1016/j.neunet.2005.01.004
Li, F. F., & Tang, Y. (2022). Multi-Sensor Fusion Boolean Bayesian Filtering for Stochastic Boolean Networks. Ieee Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/tnnls.2021.3138132
Li, G., Gao, S. S., Xia, J., Zhang, J. H., & Yang, J. H. (2020, Jun 12-14). Weighted measurement fusion Fitting Kalman Filter for Multi-sensor Nonlinear Systems. [Proceedings of 2020 ieee 4th information technology, networking, electronic and automation control conference (itnec 2020)]. 4th IEEE Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Electr Network.
Liu, Y., & Zhang, Y. (2022). A Weighted Evidence Combination Method for Multisensor Data Fusion. Journal of Internet Technology, 23(3), 553-560. https://doi.org/10.53106/160792642022052303013
Livatino, S., Guastella, D. C., Muscato, G., Rinaldi, V., Cantelli, L., Melita, C. D., Caniglia, A., Mazza, R., & Padula, G. (2021). Intuitive Robot Teleoperation Through Multi-Sensor Informed Mixed Reality Visual Aids. Ieee Access, 9, 25795-25808. https://doi.org/10.1109/access.2021.3057808
Lopez-Arreguin, A. J. R., & Montenegro, S. (2021). Machine learning in planetary rovers: A survey of learning versus classical estimation methods in terramechanics for in situ exploration. Journal of Terramechanics, 97, 1-17. https://doi.org/10.1016/j.jterra.2021.04.005
Miao, C. W., Chen, G. Z., Yan, C. L., & Wu, Y. Y. (2021). Path planning optimization of indoor mobile robot based on adaptive ant colony algorithm. Computers & Industrial Engineering, 156, Article 107230. https://doi.org/10.1016/j.cie.2021.107230
Moysis, L., Petavratzis, E., Volos, C., Nistazakis, H., & Stouboulos, L. (2020). A chaotic path planning generator based on logistic map and modulo tactics. Robotics and Autonomous Systems, 124, Article 103377. https://doi.org/10.1016/j.robot.2019.103377
Nozaki, T., & Krebs, H. I. (2022). Development of an Optical Sensor Capable of Measuring Distance, Tilt, and Contact Force. Ieee Transactions on Industrial Electronics, 69(5), 4938-4945. https://doi.org/10.1109/tie.2021.3084168
Phung, M. D., & Ha, Q. P. (2020). Motion-encoded particle swarm optimization for moving target search using UAVs. Applied Soft Computing, 97, Article 106705. https://doi.org/10.1016/j.asoc.2020.106705
Qu, D. K., Du, Z. G., Xu, D. G., & Xu, F. (2008). Research on Path Planning for a Mobile Robot. Robot(02), 97-101+106. https://doi.org/10.13973/j.cnki.robot.2008.02.002.
Qu, H., Xing, K., & Alexander, T. (2013). An improved genetic algorithm with co-evolutionary strategy for global path planning of multiple mobile robots. Neurocomputing, 120, 509-517. https://doi.org/10.1016/j.neucom.2013.04.020
Ren, Z. B., Lam, E. Y., & Zhao, J. L. (2020). Real-Time Target Detection in Visual Sensing Environments Using Deep Transfer Learning and Improved Anchor Box Generation. Ieee Access, 8, 193512-193522. https://doi.org/10.1109/access.2020.3032955
Reza, M., Dehbalaei, G., & Delshad, E. (2013). Presenting a Model for the Affordable Choice of Wiring Route in the Electrical and Telecommunications Networks in the Residential Areas Based on the Artificial Intelligence A-STAR Algorithm. Life Science Journal-Acta Zhengzhou University Overseas Edition, 10(1), 1068-1070.
Saeed, R. A., Omri, M., Abdel-Khalek, S., Ali, E. S., & Alotaibi, M. F. (2022). Optimal path planning for drones based on swarm intelligence algorithm. Neural Computing & Applications, 34(12), 10133-10155. https://doi.org/10.1007/s00521-022-06998-9
Sangeetha, V., Krishankumar, R., Ravichandran, K. S., & Kar, S. (2021). Energy-efficient green ant colony optimization for path planning in dynamic 3D environments. Soft Computing, 25(6), 4749-4769. https://doi.org/10.1007/s00500-020-05483-6
Seyitoglu, F., & Ivanov, S. (2021). Service robots as a tool for physical distancing in tourism. Current Issues in Tourism, 24(12), 1631-1634. https://doi.org/10.1080/13683500.2020.1774518
Shang, Y., Liu, B. S., Zhang, W. C., & Xu, Y. R. (1998). Autonomous Underwater Vehicles Global Path Planning Using Case-Based Learning Algorithm. JOURNAL OF HARBIN ENGINEERING UNIVERSITY(05), 4-10. https://doi.org/CNKI:SUN:HEBG.0.1998-05-000.
Sharma, V., Kbashi, H. J., & Sergeyev, S. (2021). MIMO-employed coherent photonic-radar (MIMO-Co-PHRAD) for detection and ranging. Wireless Networks, 27(4), 2549-2558. https://doi.org/10.1007/s11276-021-02605-2
Shibata, T., Fukuda, T., & Ieee. (1993, May 02-06). COORDINATIVE BEHAVIOR BY GENETIC ALGORITHM AND FUZZY IN EVOLUTIONARY MULTIAGENT SYSTEM. [Proceedings : Ieee international conference on robotics and automation, vols 1-3]. 1993 Ieee International Conf on Robotics and Automation, Atlanta, Ga.
Song, B. Y., Wang, Z. D., & Zou, L. (2021). An improved PSO algorithm for smooth path planning of mobile robots using continuous high-degree Bezier curve. Applied Soft Computing, 100, Article 106960. https://doi.org/10.1016/j.asoc.2020.106960
Stentz, A., & Ieee. (1994, May 08-13). OPTIMAL AND EFFICIENT PATH PLANNING FOR PARTIALLY-KNOWN ENVIRONMENTS.Ieee International Conference on Robotics and Automation [1994 ieee international conference on robotics and automation: Proceedings, vols 1-4]. 1994 IEEE International Conference on Robotics and Automation, San Diego, Ca.
Sun, B., Chen, W. D., & Xi, Y. G. (2005). Particle Swarm Optimization Based Global Path Planning for Mobile Robots. Control and Decision(09), 1052-1055+1060. https://doi.org/10.13195/j.cd.2005.09.94.sunb.019.
Sun, W. C., Wang, S. Y., Wu, J. C., & Du, X. (2017, Mar 25-26). An improved RGB-D SLAM algorithm. [2017 ieee 2nd advanced information technology, electronic and automation control conference (iaeac)]. 2nd IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, PEOPLES R CHINA.
Tao, Y., Gao, H., Ren, F., Chen, C. Y., Wang, T. M., Xiong, H. G., & Jiang, S. (2021). A Mobile Service Robot Global Path Planning Method Based on Ant Colony Optimization and Fuzzy Control. Applied Sciences-Basel, 11(8), Article 3605. https://doi.org/10.3390/app11083605
Zhang, P. Y., Lu, T. S., & Song, L. B. (2006). The Case-Based Learning of Motion Planning and Its SVR Implementation for Volleyball Robot. Journal of Shanghai Jiaotong University (03), 461-465. https://doi.org/10.16183/j.cnki.jsjtu.2006.03.022.
Zhang, S. C., Pu, J. X., & Si, Y. N. (2021). An Adaptive Improved Ant Colony System Based on Population Information Entropy for Path Planning of Mobile Robot. Ieee Access, 9, 24933-24945. https://doi.org/10.1109/access.2021.3056651
Zhou, H. B., Zhou, S., Yu, J., Zhang, Z. D., & Liu, Z. Z. (2020). Trajectory Optimization of Pickup Manipulator in Obstacle Environment Based on Improved Artificial Potential Field Method. Applied Sciences-Basel, 10(3), Article 935. https://doi.org/10.3390/app10030935
Published
2023-09-30
How to Cite
FAN, Luxin et al.
Review of Research Status of Autonomous Mobile Robot Environment Recognition and Path Planning Algorithms.
International Journal of Business and Technology Management, [S.l.], v. 5, n. 3, p. 396-407, sep. 2023.
ISSN 2682-7646.
Available at: <https://myjms.mohe.gov.my/index.php/ijbtm/article/view/23481>. Date accessed: 16 sep. 2024.
Section
Articles
